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System and method for deep learning based hand gesture recognition in first person view

机译:在第一人称视角中基于深度学习的手势识别的系统和方法

摘要

This disclosure relates generally to hand-gesture recognition, and more particularly to system and method for detecting interaction of 3D dynamic hand gestures with frugal AR devices. In one embodiment, a method for hand-gesture recognition includes receiving frames of a media stream of a scene captured from a FPV of a user using RGB sensor communicably coupled to a wearable AR device. The media stream includes RGB image data associated with the frames of the scene. The scene comprises a dynamic hand gesture performed by the user. Temporal information associated with the dynamic hand gesture is estimated from the RGB image data by using a deep learning model. The estimated temporal information is associated with hand poses of the user and comprises key-points identified on user's hand in the frames. Based on said temporal information, the dynamic hand gesture is classified into predefined gesture classes by using multi-layered LSTM classification network.
机译:本公开总体上涉及手势识别,并且更具体地涉及用于检测3D动态手势与节俭AR设备的交互的系统和方法。在一个实施例中,一种用于手势识别的方法包括:使用可通信地耦合到可穿戴AR设备的RGB传感器来接收从用户的FPV捕获的场景的媒体流的帧。媒体流包括与场景的帧关联的RGB图像数据。该场景包括由用户执行的动态手势。通过使用深度学习模型,从RGB图像数据中估计与动态手势相关的时间信息。估计的时间信息与用户的手势相关联,并且包括在帧中在用户的手上标识的关键点。基于所述时间信息,通过使用多层LSTM分类网络将动态手势分类为预定的手势类别。

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